Chunking with Support Vector Machines

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Chunking with Support Vector Machines (2001)

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by Taku Kudo , Yuji Matsumoto

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@MISC{Kudo01chunkingwith,
    author = {Taku Kudo and Yuji Matsumoto},
    title = {Chunking with Support Vector Machines},
    year = {2001}
}

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Abstract

We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.

Keyphrases

support vector machine high generalization performance kernel principle english base phrase input data weighted voting distinct chunk representation previous approach experimental result svmsbased system high dimensional feature space

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